bims-mascan 2023-12-17 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2023–12–17
thirty-one papers selected by
Giovanny Rodriguez Blanco, University of Edinburgh

An integrated workflow for quantitative analysis of the newly synthesized proteome.
The Emerging Potential of Advanced Targeted Mass Spectrometry to Become a Routine Tool for Protein Quantification in Biomedical Research.
Deep Learning Based Metabolite Annotation.
Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP.
The interplay of the circadian clock and metabolic tumorigenesis.
A High-Sensitivity Low-Nanoflow LC-MS Configuration for High-Throughput Sample-Limited Proteomics.
Multivariate modeling of metabolic state vulnerabilities across diverse cancer contexts reveals synthetically lethal associations.
Challenges in ESI-MS-based Untargeted Metabolomics.
Phenotypic profiling of solute carriers characterizes serine transport in cancer.
Glutaminase inhibition as potential cancer therapeutics: current status and future applications.
DeepRTAlign: toward accurate retention time alignment for large cohort mass spectrometry data analysis.
Boundary flux analysis: an emerging strategy for investigating metabolic pathway activity in large cohorts.
Sub-5-min RP-UHPLC-TIMS for high-throughput untargeted lipidomics and its application to multiple matrices.
Multi-omic diagnostics of prostate cancer in the presence of benign prostatic hyperplasia.
Data Processing of Product Ion Spectra: Redundancy of Product Ion Spectra of Small Molecules in Data-Dependent Acquisition Dataset.
Metabolic Profiles of Cancer Stem Cells and Normal Stem Cells and Their Therapeutic Significance.
Application of a capillary electrophoresis-mass spectrometry metabolomics workflow in zebrafish larvae reveals new effects of cortisol.
Lipidomics Profiling Reveals Differential Alterations after FAS Inhibition in 3D Colon Cancer Cell Culture Models.
Targeting glutamine dependence with DRP-104 inhibits proliferation and tumor growth of castration-resistant prostate cancer.
Database-Driven Spatially Resolved Lipidomics Highlights Heterogeneous Metabolic Alterations in Type 2 Diabetic Mice.
Glutamine addiction and therapeutic strategies in pancreatic cancer.
Toward Omics-Scale Quantitative Mass Spectrometry Imaging of Lipids in Brain Tissue Using a Multiclass Internal Standard Mixture.
Challenges in the Analysis of Gangliosides by LC-MS.
The first human normative ranges and biomarker performance of dimethylguanidino valeric acid isoforms in fatty liver disease.
Unexpected Differences in the Speed of Non-Malignant versus Malignant Cell Migration Reveal Differential Basal Intracellular ATP Levels.
A positive feedback loop between ZEB2 and ACSL4 regulates lipid metabolism to promote breast cancer metastasis.
A simulation-free constrained regression approach for flux estimation in isotopically nonstationary metabolic flux analysis with applications in microalgae.
Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology.
Retention time prediction and MRM validation reinforce the biomarker identification of LC-MS based phospholipidomics.
Mass Spectrometry in Proteomics: Technologies, Methods, and Research Applications for the Life Sciences.
metamedian: An R package for meta-analyzing studies reporting medians.

Nat Commun. 2023 Dec 12. 14(1): 8237

An integrated workflow for quantitative analysis of the newly synthesized proteome.

Toman Borteçen, Torsten Müller, Jeroen Krijgsveld.

The analysis of proteins that are newly synthesized upon a cellular perturbation can provide detailed insight into the proteomic response that is elicited by specific cues. This can be investigated by pulse-labeling of cells with clickable and stable-isotope-coded amino acids for the enrichment and mass spectrometric characterization of newly synthesized proteins (NSPs), however convoluted protocols prohibit their routine application. Here we report the optimization of multiple steps in sample preparation, mass spectrometry and data analysis, and we integrate them into a semi-automated workflow for the quantitative analysis of the newly synthesized proteome (QuaNPA). Reduced input requirements and data-independent acquisition (DIA) enable the analysis of triple-SILAC-labeled NSP samples, with enhanced throughput while featuring high quantitative accuracy. We apply QuaNPA to investigate the time-resolved cellular response to interferon-gamma (IFNg), observing rapid induction of targets 2 h after IFNg treatment. QuaNPA provides a powerful approach for large-scale investigation of NSPs to gain insight into complex cellular processes.

DOI: https://doi.org/10.1038/s41467-023-43919-3
Chimia (Aarau). 2022 Feb 23. 76(1-2): 81-89

The Emerging Potential of Advanced Targeted Mass Spectrometry to Become a Routine Tool for Protein Quantification in Biomedical Research.

Minia Antelo Varela, Alexander Schmidt.

  Mass spectrometry-based proteomics has become an indispensable tool for system-wide protein quantification in systems biology, biomedical research, and increasing for clinical applications. In particular, targeted mass spectrometry offers the most sensitive and reproducible quantitative detection of proteins, peptides and post-translational modifications of any currently applied mass spectrometry technique and is therefore ideally suited to generate high quality quantitative datasets. Despite these apparent advantages, targeted mass spectrometry is only slowly gaining popularity in academia and pharmaceutical industries, mainly due to the additional efforts in assay generation and manual data validation. However, with the increasing accumulation of mass spectrometry data, advances in deep learning spectral prediction for automated assay development, these obstacles can and will be considerably reduced in the near future. Here, we describe the latest technological developments in this field and discuss the emerging importance of targeted mass spectrometry for systems biology research and potential key roles in bridging biomedical discovery and clinical implementation.

Keywords:  DIA; Parallel reaction monitoring; Selected reaction monitoring; SureQuant; TOMAHAQ; Targeted mass spectrometry

DOI:  https://doi.org/10.2533/chimia.2022.81
Annu Int Conf IEEE Eng Med Biol Soc. 2023 Jul;2023 1-4

Deep Learning Based Metabolite Annotation.

Hoi Yan Katharine Chau, Hongyu Ao, Xinran Zhang, Shijinqiu Gao, Rency S Varghese, Habtom W Ressom.

Metabolite annotation is a major bottleneck in untargeted metabolomics studies by liquid chromatography coupled with mass spectrometry (LC-MS). This is in part due to the limited publicly available spectral libraries, which consist of tandem mass spectrometry (MS/MS) data acquired from just a fraction of known compounds. Machine learning and deep learning methods provide the opportunity to predict molecular fingerprints based on MS/MS data. The predicted molecular fingerprints can then be used to help rank candidate metabolite IDs obtained based on predicted formula or measured precursor m/z of the unknown metabolite. This approach is particularly useful to help annotate metabolites whose corresponding MS/MS spectra cannot be matched with those in spectral libraries. We previously reported application of a convolutional neural network (CNN) for molecular fingerprint prediction using MS/MS spectra obtained from the MoNA repository and NIST 20. In this paper, we investigate high-dimensional representation of the spectral data and molecular fingerprints to improve accuracy in molecular fingerprint prediction.

DOI: https://doi.org/10.1109/EMBC40787.2023.10341007
bioRxiv. 2023 Nov 28. pii: 2023.11.27.568927. [Epub ahead of print]

Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP.

Andrew Leduc, Luke Koury, Joshua Cantlon, Nikolai Slavov.

Single-cell proteomics by mass spectrometry (MS) allows quantifying proteins with high specificity and sensitivity. To increase its throughput, we developed nPOP, a method for parallel preparation of thousands of single cells in nanoliter volume droplets deposited on glass slides. Here, we describe its protocol with emphasis on its flexibility to prepare samples for different multiplexed MS methods. An implementation with plexDIA demonstrates accurate quantification of about 3,000 - 3,700 proteins per human cell. The protocol is implemented on the CellenONE instrument and uses readily available consumables, which should facilitate broad adoption. nPOP can be applied to all samples that can be processed to a single-cell suspension. It takes 1 or 2 days to prepare over 3,000 single cells. We provide metrics and software for quality control that can support the robust scaling of nPOP to higher plex reagents for achieving reliable high-throughput single-cell protein analysis.

DOI: https://doi.org/10.1101/2023.11.27.568927
Trends Cell Biol. 2023 Dec 06. pii: S0962-8924(23)00237-4. [Epub ahead of print]

The interplay of the circadian clock and metabolic tumorigenesis.

Zheng Wang, Leina Ma, Ying Meng, Jing Fang, Daqian Xu, Zhimin Lu.

  The circadian clock and cell metabolism are both dysregulated in cancer cells through intrinsic cell-autonomous mechanisms and external influences from the tumor microenvironment. The intricate interplay between the circadian clock and cancer cell metabolism exerts control over various metabolic processes, including aerobic glycolysis, de novo nucleotide synthesis, glutamine and protein metabolism, lipid metabolism, mitochondrial metabolism, and redox homeostasis in cancer cells. Importantly, oncogenic signaling can confer a moonlighting function on core clock genes, effectively reshaping cellular metabolism to fuel cancer cell proliferation and drive tumor growth. These interwoven regulatory mechanisms constitute a distinctive feature of cancer cell metabolism.

Keywords:  cancer metabolism; dysregulated circadian clock; moonlighting function

DOI:  https://doi.org/10.1016/j.tcb.2023.11.004
Anal Chem. 2023 Dec 13.

A High-Sensitivity Low-Nanoflow LC-MS Configuration for High-Throughput Sample-Limited Proteomics.

Runsheng Zheng, Manuel Matzinger, Rupert L Mayer, Alec Valenta, Xuefei Sun, Karl Mechtler.

This work demonstrates the utility of high-throughput nanoLC-MS and label-free quantification (LFQ) for sample-limited bottom-up proteomics analysis, including single-cell proteomics (SCP). Conditions were optimized on a 50 μm internal diameter (I.D.) column operated at 100 nL/min in the direct injection workflow to balance method sensitivity and sample throughput from 24 to 72 samples/day. Multiple data acquisition strategies were also evaluated for proteome coverage, including data-dependent acquisition (DDA), wide-window acquisition (WWA), and wide-window data-independent acquisition (WW-DIA). Analyzing 250 pg HeLa digest with a 10-min LC gradient (72 samples/day) provided >900, >1,800, and >3,000 protein group identifications for DDA, WWA, and WW-DIA, respectively. Total method cycle time was further reduced from 20 to 14.4 min (100 samples/day) by employing a trap-and-elute workflow, enabling 70% mass spectrometer utilization. The method was applied to library-free DIA analysis of single-cell samples, yielding >1,700 protein groups identified. In conclusion, this study provides a high-sensitivity, high-throughput nanoLC-MS configuration for sample-limited proteomics.

DOI: https://doi.org/10.1021/acs.analchem.3c03058
bioRxiv. 2023 Nov 29. pii: 2023.11.28.569098. [Epub ahead of print]

Multivariate modeling of metabolic state vulnerabilities across diverse cancer contexts reveals synthetically lethal associations.

Cara Abecunas, Mohammad Fallahi-Sichani.

Targeting the distinct metabolic needs of tumor cells has recently emerged as a promising strategy for cancer therapy. The heterogeneous, context-dependent nature of cancer cell metabolism, however, poses challenges in identifying effective therapeutic interventions. Here, we utilize various unsupervised and supervised multivariate modeling approaches to systematically pinpoint recurrent metabolic states within hundreds of cancer cell lines, elucidate their association with tissue lineage and growth environments, and uncover vulnerabilities linked to their metabolic states across diverse genetic and tissue contexts. We validate key findings using data from an independent set of cell lines, pharmacological screens, and via single-cell analysis of patient-derived tumors. Our analysis uncovers new synthetically lethal associations between the tumor metabolic state (e.g., oxidative phosphorylation), driver mutations (e.g., loss of tumor suppressor PTEN), and actionable biological targets (e.g., mitochondrial electron transport chain). Investigating these relationships could inform the development of more precise and context-specific, metabolism-targeted cancer therapies.

DOI: https://doi.org/10.1101/2023.11.28.569098
Chimia (Aarau). 2022 Feb 23. 76(1-2): 90-100

Challenges in ESI-MS-based Untargeted Metabolomics.

Elena Tobolkina, Víctor González-Ruiz, Isabel Meister, Miguel De Figueiredo, Davy Guillarme, Julien Boccard, Serge Rudaz.

  Untargeted metabolomics is now widely recognized as a useful tool for exploring metabolic changes taking place in biological systems under different conditions. In this article, we aim to provide a short overview of the liquid-phase separation methods hyphenated to MS to perform untargeted metabolomics of biological samples. Each approach is complemented by up-to-date literature to guide readers, as well as practical information for avoiding or fixing some of the most frequently encountered pitfalls. This article covers mainly data acquisition, but a short discussion is provided regarding signal processing and data treatment, as well as data analysis and its biological interpretation in the context of metabolomic studies.

Keywords:  Annotation; Capillary electrophoresis; Liquid chromatography; Mass spectrometry; Metabolomics; Supercritical fluid chromatography; Toxicological and doping analysis

DOI:  https://doi.org/10.2533/chimia.2022.90
Nat Metab. 2023 Dec 08.

Phenotypic profiling of solute carriers characterizes serine transport in cancer.

Vasileios Papalazarou, Alice C Newman, Alejandro Huerta-Uribe, Nathalie M Legrave, Mattia Falcone, Tong Zhang, Lynn McGarry, Dimitris Athineos, Emma Shanks, Karen Blyth, Karen H Vousden, Oliver D K Maddocks.

Serine is a vital amino acid in tumorigenesis. While cells can perform de novo serine synthesis, most transformed cells rely on serine uptake to meet their increased biosynthetic requirements. Solute carriers (SLCs), a family of transmembrane nutrient transport proteins, are the gatekeepers of amino acid acquisition and exchange in mammalian cells and are emerging as anticancer therapeutic targets; however, the SLCs that mediate serine transport in cancer cells remain unknown. Here we perform an arrayed RNAi screen of SLC-encoding genes while monitoring amino acid consumption and cell proliferation in colorectal cancer cells using metabolomics and high-throughput imaging. We identify SLC6A14 and SLC25A15 as major cytoplasmic and mitochondrial serine transporters, respectively. We also observe that SLC12A4 facilitates serine uptake. Dual targeting of SLC6A14 and either SLC25A15 or SLC12A4 diminishes serine uptake and growth of colorectal cancer cells in vitro and in vivo, particularly in cells with compromised de novo serine biosynthesis. Our results provide insight into the mechanisms that contribute to serine uptake and intracellular handling.

DOI: https://doi.org/10.1038/s42255-023-00936-2
J Enzyme Inhib Med Chem. 2024 Dec;39(1): 2290911

Glutaminase inhibition as potential cancer therapeutics: current status and future applications.

Rajath Cyriac, Kwangho Lee.

  Alterations in normal metabolic processes are defining features of cancer. Glutamine, an abundant amino acid in the human blood, plays a critical role in regulating several biosynthetic and bioenergetic pathways that support tumour growth. Glutaminolysis is a metabolic pathway that converts glutamine into various metabolites involved in the tricarboxylic acid (TCA) cycle and generates antioxidants that are vital for tumour cell survival. As glutaminase catalyses the initial step of this metabolic pathway, it is of great significance in cancer metabolism and tumour progression. Inhibition of glutaminase and targeting of glutaminolysis have emerged as promising strategies for cancer therapy. This review explores the role of glutaminases in cancer metabolism and discusses various glutaminase inhibitors developed as potential therapies for tumour regression.

Keywords:  GLS; KEAP1 mutation; anticancer; cancer metabolism; glutaminase

DOI:  https://doi.org/10.1080/14756366.2023.2290911
Nat Commun. 2023 Dec 11. 14(1): 8188

DeepRTAlign: toward accurate retention time alignment for large cohort mass spectrometry data analysis.

Yi Liu, Yun Yang, Wendong Chen, Feng Shen, Linhai Xie, Yingying Zhang, Yuanjun Zhai, Fuchu He, Yunping Zhu, Cheng Chang.

Retention time (RT) alignment is a crucial step in liquid chromatography-mass spectrometry (LC-MS)-based proteomic and metabolomic experiments, especially for large cohort studies. The most popular alignment tools are based on warping function method and direct matching method. However, existing tools can hardly handle monotonic and non-monotonic RT shifts simultaneously. Here, we develop a deep learning-based RT alignment tool, DeepRTAlign, for large cohort LC-MS data analysis. DeepRTAlign has been demonstrated to have improved performances by benchmarking it against current state-of-the-art approaches on multiple real-world and simulated proteomic and metabolomic datasets. The results also show that DeepRTAlign can improve identification sensitivity without compromising quantitative accuracy. Furthermore, using the MS features aligned by DeepRTAlign, we trained and validated a robust classifier to predict the early recurrence of hepatocellular carcinoma. DeepRTAlign provides an advanced solution to RT alignment in large cohort LC-MS studies, which is currently a major bottleneck in proteomics and metabolomics research.

DOI: https://doi.org/10.1038/s41467-023-43909-5
Curr Opin Biotechnol. 2023 Dec 06. pii: S0958-1669(23)00137-4. [Epub ahead of print]85 103027

Boundary flux analysis: an emerging strategy for investigating metabolic pathway activity in large cohorts.

Ian A Lewis.

Many biological phenotypes are rooted in metabolic pathway activity rather than the concentrations of individual metabolites. Despite this, most metabolomics studies only capture steady-state metabolism - not metabolic flux. Although sophisticated metabolic flux analysis strategies have been developed, these methods are technically challenging and difficult to implement in large-cohort studies. Recently, a new boundary flux analysis (BFA) approach has emerged that captures large-scale metabolic flux phenotypes by quantifying changes in metabolite levels in the media of cultured cells. This approach is advantageous because it is relatively easy to implement yet captures complex metabolic flux phenotypes. We describe the opportunities and challenges of BFA and illustrate how it can be harnessed to investigate a wide transect of biological phenomena.

DOI: https://doi.org/10.1016/j.copbio.2023.103027
Anal Bioanal Chem. 2023 Dec 11.

Sub-5-min RP-UHPLC-TIMS for high-throughput untargeted lipidomics and its application to multiple matrices.

Fabrizio Merciai, Manuela Giovanna Basilicata, Danila La Gioia, Emanuela Salviati, Vicky Caponigro, Tania Ciaglia, Simona Musella, Carlo Crescenzi, Eduardo Sommella, Pietro Campiglia.

  Untargeted lipidomics, with its ability to take a snapshot of the lipidome landscape, is an important tool to highlight lipid changes in pathology or drug treatment models. One of the shortcomings of most untargeted lipidomics based on UHPLC-HRMS is the low throughput, which is not compatible with large-scale screening. In this contribution, we evaluate the application of a sub-5-min high-throughput four-dimensional trapped ion mobility mass spectrometry (HT-4D-TIMS) platform for the fast profiling of multiple complex biological matrices. Human AC-16 cells and mouse brain, liver, sclera, and feces were used as samples. By using a fast 4-min RP gradient, the implementation of TIMS allows us to differentiate coeluting isomeric and isobaric lipids, with correct precursor ion isolation, avoiding co-fragmentation and chimeric MS/MS spectra. Globally, the HT-4D-TIMS allowed us to annotate 1910 different lipid species, 1308 at the molecular level and 602 at the sum composition level, covering 58 lipid subclasses, together with quantitation capability covering more than three orders of magnitude. Notably, TIMS values were highly comparable with respect to longer LC gradients (CV% = 0.39%). These results highlight how HT-4D-TIMS-based untargeted lipidomics possess high coverage and accuracy, halving the analysis time with respect to conventional UHPLC methods, and can be used for fast and accurate untargeted analysis of complex matrices to rapidly evaluate changes of lipid metabolism in disease models or drug discovery campaigns.

Keywords:  High-throughput; PASEF; Trapped ion mobility; Untargeted lipidomics

DOI:  https://doi.org/10.1007/s00216-023-05084-w
Heliyon. 2023 Dec;9(12): e22604

Multi-omic diagnostics of prostate cancer in the presence of benign prostatic hyperplasia.

Matt Spick, Ammara Muazzam, Hardev Pandha, Agnieszka Michael, Lee A Gethings, Christopher J Hughes, Nyasha Munjoma, Robert S Plumb, Ian D Wilson, Anthony D Whetton, Paul A Townsend, Nophar Geifman.

  There is an unmet need for improved diagnostic testing and risk prediction for cases of prostate cancer (PCa) to improve care and reduce overtreatment of indolent disease. Here we have analysed the serum proteome and lipidome of 262 study participants by liquid chromatography-mass spectrometry, including participants diagnosed with PCa, benign prostatic hyperplasia (BPH), or otherwise healthy volunteers, with the aim of improving biomarker specificity. Although a two-class machine learning model separated PCa from controls with sensitivity of 0.82 and specificity of 0.95, adding BPH resulted in a statistically significant decline in specificity for prostate cancer to 0.76, with half of BPH cases being misclassified by the model as PCa. A small number of biomarkers differentiating between BPH and prostate cancer were identified, including proteins in MAP Kinase pathways, as well as in lipids containing oleic acid; these may offer a route to greater specificity. These results highlight, however, that whilst there are opportunities for machine learning, these will only be achieved by use of appropriate training sets that include confounding comorbidities, especially when calculating the specificity of a test.

Keywords:  Biomarkers; Complement; LC-MS; Lipidomics; MAPK; Prostate cancer; Proteomics; Tumor progression

DOI:  https://doi.org/10.1016/j.heliyon.2023.e22604
Mass Spectrom (Tokyo). 2023 ;12(1): A0138

Data Processing of Product Ion Spectra: Redundancy of Product Ion Spectra of Small Molecules in Data-Dependent Acquisition Dataset.

Fumio Matsuda.

  Non-targeted metabolome analysis studies comprehensively acquire product ion spectra from the observed ions by the data-dependent acquisition (DDA) mode of tandem mass spectrometry (MS). A DDA dataset redundantly contains closely similar product ion spectra of metabolites commonly existing among the biological samples analyzed in a metabolome study. Moreover, a single DDA data file often includes two or more closely similar raw spectra obtained from an identical precursor ion. The redundancy of product ion spectra has been used to generate an averaged product ion spectrum from a set of similar product ion spectra recorded in a DDA dataset. The spectral averaging improved the accuracy of m/z values and signal-to-noise levels of product ion spectra. However, the origins of redundancy, variations among datasets, and these effects on the spectral averaging procedure needed to be better characterized. This study investigated the nature of the redundancy by comparing the averaging results of eight DDA datasets of non-targeted metabolomics studies. The comparison revealed a significant variation in redundancy among datasets. The DDA datasets obtained by the quadrupole (Q)-Orbitrap-MS datasets had more significant intrafile redundancy than that of the Q-time-of-flight-MS. For evaluating the similarity score between two production spectra, the optimal threshold level of the cosine-product method was approximately 0.8-0.9. Moreover, contamination of biological samples such as plasticizers was another origin of spectral redundancy. The results will be the basis for further development of methods for processing of product ion spectra data. Copyright © 2023 Fumio Matsuda. This is an open-access article distributed under the terms of Creative Commons Attribution Non-Commercial 4.0 International License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. Please cite this article as: Mass Spectrom (Tokyo) 2023; 12(1): A0138.

Keywords:  data-dependent acquisition mode; metabolomics; redundancy; spectra averaging; spectra similarity

DOI:  https://doi.org/10.5702/massspectrometry.A0138
Cells. 2023 Nov 22. pii: 2686. [Epub ahead of print]12(23):

Metabolic Profiles of Cancer Stem Cells and Normal Stem Cells and Their Therapeutic Significance.

Ioannis Stouras, Maria Vasileiou, Panagiotis F Kanatas, Eleni Tziona, Christina Tsianava, Stamatis Theocharis.

  Cancer stem cells (CSCs) are a rare cancer cell population, responsible for the facilitation, progression, and resistance of tumors to therapeutic interventions. This subset of cancer cells with stemness and tumorigenic properties is organized in niches within the tumor microenvironment (TME) and presents altered regulation in a variety of metabolic pathways, including glycolysis, oxidative phosphorylation (OXPHOS), as well as lipid, amino acid, and iron metabolism. CSCs exhibit similarities as well as differences when comparedto normal stem cells, but also possess the ability of metabolic plasticity. In this review, we summarize the metabolic characteristics of normal, non-cancerous stem cells and CSCs. We also highlight the significance and implications of interventions targeting CSC metabolism to potentially achieve more robust clinical responses in the future.

Keywords:  amino acid metabolism; cancerstem cells; glycolysis; lipid metabolism; metabolism; oxidative phosphorylation; stem cells

DOI:  https://doi.org/10.3390/cells12232686
Electrophoresis. 2023 Dec 10.

Application of a capillary electrophoresis-mass spectrometry metabolomics workflow in zebrafish larvae reveals new effects of cortisol.

Marlien van Mever, Maricruz Mamani-Huanca, Erin Faught, Ángeles López-Gonzálvez, Thomas Hankemeier, Coral Barbas, Marcel J M Schaaf, Rawi Ramautar.

  In contemporary biomedical research, the zebrafish (Danio rerio) is increasingly considered a model system, as zebrafish embryos and larvae can (potentially) fill the gap between cultured cells and mammalian animal models, because they can be obtained in large numbers, are small and can easily be manipulated genetically. Given that capillary electrophoresis-mass spectrometry (CE-MS) is a useful analytical separation technique for the analysis of polar ionogenic metabolites in biomass-limited samples, the aim of this study was to develop and assess a CE-MS-based analytical workflow for the profiling of (endogenous) metabolites in extracts from individual zebrafish larvae and pools of small numbers of larvae. The developed CE-MS workflow was used to profile metabolites in extracts from pools of 1, 2, 4, 8, 12, 16, 20, and 40 zebrafish larvae. For six selected endogenous metabolites, a linear response (R2 > 0.98) for peak areas was obtained in extracts from these pools. The repeatability was satisfactory, with inter-day relative standard deviation values for peak area of 9.4%-17.7% for biological replicates (n = 3 over 3 days). Furthermore, the method allowed the analysis of over 70 endogenous metabolites in a pool of 12 zebrafish larvae, and 29 endogenous metabolites in an extract from only 1 zebrafish larva. Finally, we applied the optimized CE-MS workflow to identify potential novel targets of the mineralocorticoid receptor in mediating the effects of cortisol.

Keywords:  capillary electrophoresis-mass spectrometry; glucocorticoid receptor; metabolomics; mineralocorticoid receptor; zebrafish larvae

DOI:  https://doi.org/10.1002/elps.202300186
J Proteome Res. 2023 Dec 08.

Lipidomics Profiling Reveals Differential Alterations after FAS Inhibition in 3D Colon Cancer Cell Culture Models.

Brian D Fries, Fernando Tobias, Yijia Wang, Joseph H Holbrook, Amanda B Hummon.

  Cancerous cells synthesize most of their lipids de novo to keep up with their rapid growth and proliferation. Fatty acid synthase (FAS) is a key enzyme in the lipogenesis pathway that is upregulated in many cancers and has gained popularity as a druggable target of interest for cancer treatment. The first FAS inhibitor discovered, cerulenin, initially showed promise for chemotherapeutic purposes until it was observed that it had adverse side effects in mice. TVB-2640 (Denifanstat) is part of the newer generation of inhibitors. With multiple generations of FAS inhibitors being developed, it is vital to understand their distinct molecular downstream effects to elucidate potential interactions in the clinic. Here, we profile the lipidome of two different colorectal cancer (CRC) spheroids treated with a generation 1 inhibitor (cerulenin) or a generation 2 inhibitor (TVB-2640). We observe that the cerulenin causes drastic changes to the spheroid morphology as well as alterations to the lipid droplets found within CRC spheroids. TVB-2640 causes higher abundances of polyunsaturated fatty acids (PUFAs) whereas cerulenin causes a decreased abundance of PUFAs. The increase in PUFAs in TVB-2640 exposed spheroids indicates it is causing cells to die via a ferroptotic mechanism rather than a conventional apoptotic or necrotic mechanism.

Keywords:  FAS inhibition; apoptosis; colon cancer; ferroptosis; lipidomics; spheroids

DOI:  https://doi.org/10.1021/acs.jproteome.3c00593
Prostate. 2023 Dec 12.

Targeting glutamine dependence with DRP-104 inhibits proliferation and tumor growth of castration-resistant prostate cancer.

David Moon, J Spencer Hauck, Xue Jiang, Holly Quang, Lingfan Xu, Fan Zhang, Xia Gao, Robert Wild, Jeffrey I Everitt, Everardo Macias, Yiping He, Jiaoti Huang.

   BACKGROUND: Prostate cancer (PCa) continues to be one of the leading causes of cancer deaths in men. While androgen deprivation therapy is initially effective, castration-resistant PCa (CRPC) often recurs and has limited treatment options. Our previous study identified glutamine metabolism to be critical for CRPC growth. The glutamine antagonist 6-diazo-5-oxo-l-norleucine (DON) blocks both carbon and nitrogen pathways but has dose-limiting toxicity. The prodrug DRP-104 is expected to be preferentially converted to DON in tumor cells to inhibit glutamine utilization with minimal toxicity. However, CRPC cells' susceptibility to DRP-104 remains unclear.
METHODS: Human PCa cell lines (LNCaP, LAPC4, C4-2/MDVR, PC-3, 22RV1, NCI-H660) were treated with DRP-104, and effects on proliferation and cell death were assessed. Unbiased metabolic profiling and isotope tracing evaluated the effects of DRP-104 on glutamine pathways. Efficacy of DRP-104 in vivo was evaluated in a mouse xenograft model of neuroendocrine PCa, NCI-H660.
RESULTS: DRP-104 inhibited proliferation and induced apoptosis in CRPC cell lines. Metabolite profiling showed decreases in the tricarboxylic acid cycle and nucleotide synthesis metabolites. Glutamine isotope tracing confirmed the blockade of both carbon pathway and nitrogen pathways. DRP-104 treated CRPC cells were rescued by the addition of nucleosides. DRP-104 inhibited neuroendocrine PCa xenograft growth without detectable toxicity.
CONCLUSIONS: The prodrug DRP-104 blocks glutamine carbon and nitrogen utilization, thereby inhibiting CRPC growth and inducing apoptosis. Targeting glutamine metabolism pathways with DRP-104 represents a promising therapeutic strategy for CRPC.

Keywords:  DRP-104; cancer metabolism; castration-resistant

DOI:  https://doi.org/10.1002/pros.24654
Anal Chem. 2023 Dec 13.

Database-Driven Spatially Resolved Lipidomics Highlights Heterogeneous Metabolic Alterations in Type 2 Diabetic Mice.

Xinzhu Li, Qingce Zang, Ying Zhu, Xinyi Tu, Jialin Liu, Ting Li, Shiyu Zhu, Lingzhi Wang, Zeper Abliz, Ruiping Zhang.

Spatially resolved lipidomics is pivotal for detecting and interpreting lipidomes within spatial contexts using the mass spectrometry imaging (MSI) technique. However, comprehensive and efficient lipid identification in MSI remains challenging. Herein, we introduce a high-coverage, database-driven approach combined with air-flow-assisted desorption electrospray ionization (AFADESI)-MSI to generate spatial lipid profiles across whole-body mice. Using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS), we identified 2868 unique lipids in the serum and various organs of mice. Subsequently, we systematically evaluated the distinct ionization properties of the lipids between LC-MS and MSI and created a detailed MSI database containing 14 123 ions. This method enabled the visualization of aberrant fatty acid and phospholipid metabolism across organs in a diabetic mouse model. As a powerful extension incorporated into the MSIannotator tool, our strategy facilitates the rapid and accurate annotation of lipids, providing new research avenues for probing spatially resolved heterogeneous metabolic changes in response to diseases.

DOI: https://doi.org/10.1021/acs.analchem.3c03765
World J Gastrointest Oncol. 2023 Nov 15. 15(11): 1852-1863

Glutamine addiction and therapeutic strategies in pancreatic cancer.

Lin-Lin Ren, Tao Mao, Pin Meng, Li Zhang, Hong-Yun Wei, Zi-Bin Tian.

  Pancreatic cancer remains one of the most lethal diseases worldwide owing to its late diagnosis, early metastasis, and poor prognosis. Because current therapeutic options are limited, there is an urgent need to investigate novel targeted treatment strategies. Pancreatic cancer faces significant metabolic challenges, principally hypoxia and nutrient deprivation, due to specific microenvironmental constraints, including an extensive desmoplastic stromal reaction. Pancreatic cancer cells have been shown to rewire their metabolism and energy production networks to support rapid survival and proliferation. Increased glucose uptake and glycolytic pathway activity during this process have been extensively described. However, growing evidence suggests that pancreatic cancer cells are glutamine addicted. As a nitrogen source, glutamine directly (or indirectly via glutamate conversion) contributes to many anabolic processes in pancreatic cancer, including amino acids, nucleobases, and hexosamine biosynthesis. It also plays an important role in redox homeostasis, and when converted to α-ketoglutarate, glutamine serves as an energy and anaplerotic carbon source, replenishing the tricarboxylic acid cycle intermediates. The present study aims to provide a comprehensive overview of glutamine metabolic reprogramming in pancreatic cancer, focusing on potential therapeutic approaches targeting glutamine metabolism in pancreatic cancer.

Keywords:  Cancer treatment; Glutamine metabolism; Pancreatic cancer; Therapeutic strategies

DOI:  https://doi.org/10.4251/wjgo.v15.i11.1852
Anal Chem. 2023 Dec 11.

Toward Omics-Scale Quantitative Mass Spectrometry Imaging of Lipids in Brain Tissue Using a Multiclass Internal Standard Mixture.

Michiel Vandenbosch, Shadrack M Mutuku, Maria José Q Mantas, Nathan H Patterson, Tucker Hallmark, Marc Claesen, Ron M A Heeren, Nathan G Hatcher, Nico Verbeeck, Kim Ekroos, Shane R Ellis.

Mass spectrometry imaging (MSI) has accelerated our understanding of lipid metabolism and spatial distribution in tissues and cells. However, few MSI studies have approached lipid imaging quantitatively and those that have focused on a single lipid class. We overcome this limitation by using a multiclass internal standard (IS) mixture sprayed homogeneously over the tissue surface with concentrations that reflect those of endogenous lipids. This enabled quantitative MSI (Q-MSI) of 13 lipid classes and subclasses representing almost 200 sum-composition lipid species using both MALDI (negative ion mode) and MALDI-2 (positive ion mode) and pixel-wise normalization of each lipid species in a manner analogous to that widely used in shotgun lipidomics. The Q-MSI approach covered 3 orders of magnitude in dynamic range (lipid concentrations reported in pmol/mm2) and revealed subtle changes in distribution compared to data without normalization. The robustness of the method was evaluated by repeating experiments in two laboratories using both timsTOF and Orbitrap mass spectrometers with an ∼4-fold difference in mass resolution power. There was a strong overall correlation in the Q-MSI results obtained by using the two approaches. Outliers were mostly rationalized by isobaric interferences or the higher sensitivity of one instrument for a particular lipid species. These data provide insight into how the mass resolving power can affect Q-MSI data. This approach opens up the possibility of performing large-scale Q-MSI studies across numerous lipid classes and subclasses and revealing how absolute lipid concentrations vary throughout and between biological tissues.

DOI: https://doi.org/10.1021/acs.analchem.3c02724
Chimia (Aarau). 2022 Feb 23. 76(1-2): 109-113

Challenges in the Analysis of Gangliosides by LC-MS.

Thomas Muggli, Claudia Bühr, Stefan Schürch.

  Gangliosides are a family of conjugates consisting of a polar sialoglycan head group and a hydrophobic ceramide tail. Gangliosides are of high abundance in neuronal tissues and are involved in numerous biological processes, such as cell-cell recognition, adhesion, and signal transduction. Alteration of the ganglioside profile is associated with various neurodegenerative diseases and there is indication that gangliosides are involved in the pathogenesis of Parkinson's and Huntington's disease. The development of refined methods for the analysis of gangliosides by high-performance liquid chromatography coupled to mass spectrometry (HPLC-MS) has supported research with qualitative and quantitative data. However, the amphiphilic character of gangliosides renders their separation and mass spectrometric analysis challenging. In this article, the strengths of hydrophilic interaction liquid chromatography (HILIC) for baseline separation of gangliosides, including two structural isomers, and their structural characterization by tandem mass spectrometry are demonstrated. The importance of ion source parameter optimization is highlighted to prevent misleading ganglioside transformation due to in-source dissociation.

Keywords:  Gangliosides; Glycosphingolipids; HILIC; In-source dissociation; Mass spectrometry

DOI:  https://doi.org/10.2533/chimia.2022.109
Pathology. 2023 Nov 23. pii: S0031-3025(23)00296-9. [Epub ahead of print]

The first human normative ranges and biomarker performance of dimethylguanidino valeric acid isoforms in fatty liver disease.

Sami Qadri, XiaoSuo Wang, Collin Tran, Michael Fitzpatrick, Paul Bonnitcha, David Sullivan, Hannele Yki-Järvinen, John F O'Sullivan.

  We have recently determined dimethylguanidino valeric acid (DMGV) to be a novel biomarker of liver injury in non-alcoholic fatty liver disease (NAFLD) and an independent predictor of incident diabetes over a decade in advance. DMGV consists of two stereo-isomers, asymmetric dimethylguanidino valeric acid (ADGV) and symmetric dimethylguanidino valeric acid (SDGV). Here we report, for the first time, the upper limits of normal of both isomers in humans at the accepted 5.56% liver fat threshold for NAFLD, determined using in vivo magnetic resonance spectroscopy. We performed independent and blinded comparative analyses of ADGV and SDGV levels using two different liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods in (A) our laboratory, and (B) the New South Wales Chemical Pathology state laboratory, using unique columns, LC-MS/MS equipment, extraction protocols and normalisation approaches. Despite these differences, each laboratory reported consistent absolute concentrations across a range of liver fat percentages. We next determined the diagnostic performance of SDGV compared to ADGV in a cohort of 268 individuals with liver fat measurements. In derivation-validation analyses we determined rule-in/rule-out thresholds and the concentration of SDGV that provides optimal performance across sensitivity and specificity for the identification of NAFLD. In conclusion, we have herein determined for the first time the true human plasma reference range of both isoforms of an emerging novel biomarker of NAFLD, at the accepted upper normal threshold of liver fat. We have also identified that SDGV is the isoform with the best diagnostic performance and determined the optimal cut-point for its detection of NAFLD.

Keywords:  ADGV; Liver fat; SDGV; biomarkers; diagnosis; liquid chromatography; magnetic resonance spectroscopy; metabolic dysfunction-associated steatotic liver disease; non-alcoholic fatty liver disease; reference values; tandem mass spectrometry

DOI:  https://doi.org/10.1016/j.pathol.2023.10.006
Cancers (Basel). 2023 Nov 22. pii: 5519. [Epub ahead of print]15(23):

Unexpected Differences in the Speed of Non-Malignant versus Malignant Cell Migration Reveal Differential Basal Intracellular ATP Levels.

Bareun Kim, Anthony T Lopez, Indhujah Thevarajan, Maria F Osuna, Monica Mallavarapu, Boning Gao, Jihan K Osborne.

  Cellular locomotion is required for survival, fertility, proper embryonic development, regeneration, and wound healing. Cell migration is a major component of metastasis, which accounts for two-thirds of all solid tumor deaths. While many studies have demonstrated increased energy requirements, metabolic rates, and migration of cancer cells compared with normal cells, few have systematically compared normal and cancer cell migration as well as energy requirements side by side. Thus, we investigated how non-malignant and malignant cells migrate, utilizing several cell lines from the breast and lung. Initial screening was performed in an unbiased high-throughput manner for the ability to migrate/invade on collagen and/or Matrigel. We unexpectedly observed that all the non-malignant lung cells moved significantly faster than cells derived from lung tumors regardless of the growth media used. Given the paradigm-shifting nature of our discovery, we pursued the mechanisms that could be responsible. Neither mass, cell doubling, nor volume accounted for the individual speed and track length of the normal cells. Non-malignant cells had higher levels of intracellular ATP at premigratory-wound induction stages. Meanwhile, cancer cells also increased intracellular ATP at premigratory-wound induction, but not to the levels of the normal cells, indicating the possibility for further therapeutic investigation.

Keywords:  cancer cell migration; cell migration; normal epithelial cell motility

DOI:  https://doi.org/10.3390/cancers15235519
Elife. 2023 12 11. pii: RP87510. [Epub ahead of print]12

A positive feedback loop between ZEB2 and ACSL4 regulates lipid metabolism to promote breast cancer metastasis.

Jiamin Lin, Pingping Zhang, Wei Liu, Guorong Liu, Juan Zhang, Min Yan, Yuyou Duan, Na Yang.

  Lipid metabolism plays a critical role in cancer metastasis. However, the mechanisms through which metastatic genes regulate lipid metabolism remain unclear. Here, we describe a new oncogenic-metabolic feedback loop between the epithelial-mesenchymal transition transcription factor ZEB2 and the key lipid enzyme ACSL4 (long-chain acyl-CoA synthetase 4), resulting in enhanced cellular lipid storage and fatty acid oxidation (FAO) to drive breast cancer metastasis. Functionally, depletion of ZEB2 or ACSL4 significantly reduced lipid droplets (LDs) abundance and cell migration. ACSL4 overexpression rescued the invasive capabilities of the ZEB2 knockdown cells, suggesting that ACSL4 is crucial for ZEB2-mediated metastasis. Mechanistically, ZEB2-activated ACSL4 expression by directly binding to the ACSL4 promoter. ACSL4 binds to and stabilizes ZEB2 by reducing ZEB2 ubiquitination. Notably, ACSL4 not only promotes the intracellular lipogenesis and LDs accumulation but also enhances FAO and adenosine triphosphate production by upregulating the FAO rate-limiting enzyme CPT1A (carnitine palmitoyltransferase 1 isoform A). Finally, we demonstrated that ACSL4 knockdown significantly reduced metastatic lung nodes in vivo. In conclusion, we reveal a novel positive regulatory loop between ZEB2 and ACSL4, which promotes LDs storage to meet the energy needs of breast cancer metastasis, and identify the ZEB2-ACSL4 signaling axis as an attractive therapeutic target for overcoming breast cancer metastasis.

Keywords:  cancer biology; ACSL4; lipid metabolism; lipid droplets; cancer metastasis; EMT; ZEB2

DOI:  https://doi.org/10.7554/eLife.87510
Front Plant Sci. 2023 ;14 1140829

A simulation-free constrained regression approach for flux estimation in isotopically nonstationary metabolic flux analysis with applications in microalgae.

Anika Küken, Haim Treves, Zoran Nikoloski.

   Introduction: Flux phenotypes from different organisms and growth conditions allow better understanding of differential metabolic networks functions. Fluxes of metabolic reactions represent the integrated outcome of transcription, translation, and post-translational modifications, and directly affect growth and fitness. However, fluxes of intracellular metabolic reactions cannot be directly measured, but are estimated via metabolic flux analysis (MFA) that integrates data on isotope labeling patterns of metabolites with metabolic models. While the application of metabolomics technologies in photosynthetic organisms have resulted in unprecedented data from 13CO2-labeling experiments, the bottleneck in flux estimation remains the application of isotopically nonstationary MFA (INST-MFA). INST-MFA entails fitting a (large) system of coupled ordinary differential equations, with metabolite pools and reaction fluxes as parameters. Here, we focus on the Calvin-Benson cycle (CBC) as a key pathway for carbon fixation in photosynthesizing organisms and ask if approaches other than classical INST-MFA can provide reliable estimation of fluxes for reactions comprising this pathway.
Methods: First, we show that flux estimation with the labeling patterns of all CBC intermediates can be formulated as a single constrained regression problem, avoiding the need for repeated simulation of time-resolved labeling patterns.
Results: We then compare the flux estimates of the simulation-free constrained regression approach with those obtained from the classical INST-MFA based on labeling patterns of metabolites from the microalgae Chlamydomonas reinhardtii, Chlorella sorokiniana and Chlorella ohadii under different growth conditions.
Discussion: Our findings indicate that, in data-rich scenarios, simulation-free regression-based approaches provide a suitable alternative for flux estimation from classical INST-MFA since we observe a high qualitative agreement (rs=0.89) to predictions obtained from INCA, a state-of-the-art tool for INST-MFA.

Keywords:  13C labeling; INST-MFA; algae; metabolic flux analysis; regression

DOI:  https://doi.org/10.3389/fpls.2023.1140829
Nat Commun. 2023 Dec 13. 14(1): 8260

Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology.

Thomas Hu, Mayar Allam, Shuangyi Cai, Walter Henderson, Brian Yueh, Aybuke Garipcan, Anton V Ievlev, Maryam Afkarian, Semir Beyaz, Ahmet F Coskun.

Metabolic reprogramming in cancer and immune cells occurs to support their increasing energy needs in biological tissues. Here we propose Single Cell Spatially resolved Metabolic (scSpaMet) framework for joint protein-metabolite profiling of single immune and cancer cells in male human tissues by incorporating untargeted spatial metabolomics and targeted multiplexed protein imaging in a single pipeline. We utilized the scSpaMet to profile cell types and spatial metabolomic maps of 19507, 31156, and 8215 single cells in human lung cancer, tonsil, and endometrium tissues, respectively. The scSpaMet analysis revealed cell type-dependent metabolite profiles and local metabolite competition of neighboring single cells in human tissues. Deep learning-based joint embedding revealed unique metabolite states within cell types. Trajectory inference showed metabolic patterns along cell differentiation paths. Here we show scSpaMet's ability to quantify and visualize the cell-type specific and spatially resolved metabolic-protein mapping as an emerging tool for systems-level understanding of tissue biology.

DOI: https://doi.org/10.1038/s41467-023-43917-5
Analyst. 2023 Dec 11.

Retention time prediction and MRM validation reinforce the biomarker identification of LC-MS based phospholipidomics.

Jiangang Zhang, Yu Zhou, Juan Lei, Xudong Liu, Nan Zhang, Lei Wu, Yongsheng Li.

Dysfunctional lipid metabolism plays a crucial role in the development and progression of various diseases. Accurate measurement of lipidomes can help uncover the complex interactions between genes, proteins, and lipids in health and diseases. The prediction of retention time (RT) has become increasingly important in both targeted and untargeted metabolomics. However, the potential impact of RT prediction on targeted LC-MS based lipidomics is still not fully understood. Herein, we propose a simplified workflow for predicting RT in phospholipidomics. Our approach involves utilizing the fatty acyl chain length or carbon-carbon double bond (DB) number in combination with multiple reaction monitoring (MRM) validation. We found that our model's predictive capacity for RT was comparable to that of a publicly accessible program (QSRR Automator). Additionally, MRM validation helped in further mitigating the interference in signal recognition. Using this developed workflow, we conducted phospholipidomics of sorafenib resistant hepatocellular carcinoma (HCC) cell lines, namely MHCC97H and Hep3B. Our findings revealed an abundance of monounsaturated fatty acyl (MUFA) or polyunsaturated fatty acyl (PUFA) phospholipids in these cell lines after developing drug resistance. In both cell lines, a total of 29 lipids were found to be co-upregulated and 5 lipids were co-downregulated. Further validation was conducted on seven of the upregulated lipids using an independent dataset, which demonstrates the potential for translation of the established workflow or the lipid biomarkers.

DOI: https://doi.org/10.1039/d3an01735d
Chimia (Aarau). 2022 Feb 23. 76(1-2): 73-80

Mass Spectrometry in Proteomics: Technologies, Methods, and Research Applications for the Life Sciences.

Paolo Nanni, Peter Gehrig, Ralph Schlapbach.

  Mass spectrometry is a powerful tool in the hand of life science researchers, who constantly develop and apply new methods for the investigation of biomolecules, such as proteins, peptides, metabolites, lipids, and glycans. In this review, we will discuss the importance of mass spectrometry for the life science sector, with a special focus on the most relevant current applications in the field of proteomics. Moreover, we will comment on the factors that research groups should consider when setting up a mass spectrometry laboratory, and on the fundamental role played by academic core facilities and industrial service providers.

Keywords:  Core facilities; Life sciences; Mass spectrometry; Protein analysis; Proteomics

DOI:  https://doi.org/10.2533/chimia.2022.73
Res Synth Methods. 2023 Dec 10.

metamedian: An R package for meta-analyzing studies reporting medians.

Sean McGrath, XiaoFei Zhao, Omer Ozturk, Stephan Katzenschlager, Russell Steele, Andrea Benedetti.

  When performing an aggregate data meta-analysis of a continuous outcome, researchers often come across primary studies that report the sample median of the outcome. However, standard meta-analytic methods typically cannot be directly applied in this setting. In recent years, there has been substantial development in statistical methods to incorporate primary studies reporting sample medians in meta-analysis, yet there are currently no comprehensive software tools implementing these methods. In this paper, we present the metamedian R package, a freely available and open-source software tool for meta-analyzing primary studies that report sample medians. We summarize the main features of the software and illustrate its application through real data examples involving risk factors for a severe course of COVID-19.

Keywords:  R package; median; meta-analysis; metamedian

DOI:  https://doi.org/10.1002/jrsm.1686